The document provides an overview of credit scoring and scorecard development. It discusses:
- The objectives of credit scoring in assessing credit risk and forecasting good/bad applicants.
- The types of clients that are categorized for scoring, including good, bad, indeterminate, insufficient, excluded, and rejected.
- The research objectives and challenges in building statistical models to assign risk scores and monitor model performance.
- The research methodology involving data partitioning, variable binning, scorecard modeling using logistic regression, and scorecard evaluation metrics like KS, Gini, and lift.
In this chapter, our goal is to introduce the foundational principles of supervised learning. As we progress, we place particular emphasis on both regression and classification techniques, offering learners a more comprehensive perspective on the practical application of these methodologies in real-world scenarios. By the end of this chapter, learners will not only possess a robust understanding of the core principles but will also be armed with valuable insights into the tangible applications of supervised learning. This knowledge empowers them to skillfully navigate and leverage the full potential of this influential paradigm within the vast expanse of machine learning.
IRJET - Finger Vein Extraction and Authentication System for ATMIRJET Journal
This document summarizes a research paper on a finger vein extraction and authentication system for ATMs. The system uses repeated line tracking during feature extraction to improve the analysis of 256 pixels in finger vein images. During preprocessing, images undergo binarization, edge detection to isolate the finger region of interest, and enhancement. Features are then extracted using the repeated line tracking before classification with support vector machines. The system was tested on images from 30 subjects and achieved a peak signal to noise ratio of 78.1443 for identification, demonstrating its potential for biometric authentication applications like ATMs.
Dimensionality Reduction and feature extraction.pptxSivam Chinna
Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its intrinsic dimension.
The document provides information about the ITAB (IT Aptitude Battery), which is a set of interactive tests that measure fluid intelligence through problem-solving tasks. It describes the two main tests in the battery: the Hidden Target Test, which involves finding a hidden target on a grid, and the Battery Test, which involves identifying defective batteries. It notes that the ITAB measures how well individuals incorporate feedback, acquire skills efficiently, and develop their skill level during the assessment. It also discusses how the ITAB scores relate to and can provide incremental validity over the ASVAB for predicting technical training outcomes in the U.S. Navy.
AMAZON STOCK PRICE PREDICTION BY USING SMLTIRJET Journal
This document discusses predicting stock prices of Amazon (AMZN) stock using machine learning algorithms. It evaluates KNN, SVR, elastic net, and linear regression algorithms and finds that linear regression has the highest accuracy based on precision, recall, and F1 score metrics. The document provides details on each algorithm and compares their performance on this stock price prediction task. Linear regression builds a linear model to predict the dependent variable (stock price) based on independent variables and is shown to outperform the other supervised learning algorithms for this specific prediction problem.
The document provides an overview of credit scoring and scorecard development. It discusses:
- The objectives of credit scoring in assessing credit risk and forecasting good/bad applicants.
- The types of clients that are categorized for scoring, including good, bad, indeterminate, insufficient, excluded, and rejected.
- The research objectives and challenges in building statistical models to assign risk scores and monitor model performance.
- The research methodology involving data partitioning, variable binning, scorecard modeling using logistic regression, and scorecard evaluation metrics like KS, Gini, and lift.
In this chapter, our goal is to introduce the foundational principles of supervised learning. As we progress, we place particular emphasis on both regression and classification techniques, offering learners a more comprehensive perspective on the practical application of these methodologies in real-world scenarios. By the end of this chapter, learners will not only possess a robust understanding of the core principles but will also be armed with valuable insights into the tangible applications of supervised learning. This knowledge empowers them to skillfully navigate and leverage the full potential of this influential paradigm within the vast expanse of machine learning.
IRJET - Finger Vein Extraction and Authentication System for ATMIRJET Journal
This document summarizes a research paper on a finger vein extraction and authentication system for ATMs. The system uses repeated line tracking during feature extraction to improve the analysis of 256 pixels in finger vein images. During preprocessing, images undergo binarization, edge detection to isolate the finger region of interest, and enhancement. Features are then extracted using the repeated line tracking before classification with support vector machines. The system was tested on images from 30 subjects and achieved a peak signal to noise ratio of 78.1443 for identification, demonstrating its potential for biometric authentication applications like ATMs.
Dimensionality Reduction and feature extraction.pptxSivam Chinna
Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its intrinsic dimension.
The document provides information about the ITAB (IT Aptitude Battery), which is a set of interactive tests that measure fluid intelligence through problem-solving tasks. It describes the two main tests in the battery: the Hidden Target Test, which involves finding a hidden target on a grid, and the Battery Test, which involves identifying defective batteries. It notes that the ITAB measures how well individuals incorporate feedback, acquire skills efficiently, and develop their skill level during the assessment. It also discusses how the ITAB scores relate to and can provide incremental validity over the ASVAB for predicting technical training outcomes in the U.S. Navy.
AMAZON STOCK PRICE PREDICTION BY USING SMLTIRJET Journal
This document discusses predicting stock prices of Amazon (AMZN) stock using machine learning algorithms. It evaluates KNN, SVR, elastic net, and linear regression algorithms and finds that linear regression has the highest accuracy based on precision, recall, and F1 score metrics. The document provides details on each algorithm and compares their performance on this stock price prediction task. Linear regression builds a linear model to predict the dependent variable (stock price) based on independent variables and is shown to outperform the other supervised learning algorithms for this specific prediction problem.
Study on Evaluation of Venture Capital Based onInteractive Projection Algorithminventionjournals
International Journal of Business and Management Invention (IJBMI) is an international journal intended for professionals and researchers in all fields of Business and Management. IJBMI publishes research articles and reviews within the whole field Business and Management, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Week 2 Individual Assignment 2: Quantitative Analysis of Credit -
Solution
s
This assignment is based on the data we used during our two live sessions, but it has been updated to include a splitting variable (credit2.xlsx). In the spreadsheet under the tab “Data," you will find data
pertaining to 1,000 personal loan accounts. The tab “Data Dictionary” contains a description of what the various variables mean.
As a part of a new credit application, the company collects information about the applicant. The company then decides an amount of the credit extended (the variable CREDIT_EXTENDED). For these 1,000 accounts, we also have information on how profitable each account turned out to be (the variable NPV). A negative value indicates a net loss, and this typically happens when the debtor defaults on his/her payments.
The goal in this assignment is to investigate how one can use this data to better manage the bank's credit extension program. Specifically, our goal is to develop a classification model to classify a new credit account as “profitable” or “not profitable." Secondly we want to compare its performance in the context of decision support to a linear regression model that predicts NPV directly.
Please answer all the questions. Supply supporting documentation and show calculations as
needed. Please submit a single, well-formatted PDF or Word file. The instructor should not need to go searching for your answers! In addition, please upload an Excel file with your model outputs – the file will not be graded, but will help the instructor give you feedback, if your model differs substantially from the solutions.
For extra assistance, you may want to access the tutorials located on the course resource center page.Data Preparation
The data preparation repeats the steps from the live session:
a) The goal is to predict whether or not a new credit will result in a profitable account. Create a new variable to use as the dependent variable.
b) Create dummy variables for all categorical variables with more than 2 values (or if you prefer, you can sort your variables into numerical and categorical when you run the model).
c) Split the data into 2 parts using the splitting variable that has been added to the data set. This is to ensure a more balanced split between the validation and training samples. Note that Analytic Solver Data Mining only allows 50 columns in the analysis, so leave out your base dummies (if you created them) when partitioning. After the data partition, you should have 666 rows in your training data and 334 in your validation data.
The Assignment
1. Applying Logistic Regression
If one fits a Logistic Regression Model using all the independent variables, one observes a) a gap in the classification performance between the training data and the validation data, and b) very
high p-values for some of the variables. The performance gap between the training and validation may be a sign of overfitting, and the high p-values may b ...
FREE- REFERENCE IMAGE QUALITY ASSESSMENT FRAMEWORK USING METRICS FUSION AND D...sipij
The document presents a new no-reference image quality assessment framework that uses metrics fusion and dimensionality reduction. It extracts features from three existing no-reference metrics operating in different domains (DCT, DWT, spatial). Singular value decomposition is used to select the most relevant features, which are then input to a relevance vector machine to predict overall quality scores. Experimental results on two databases show the proposed method performs well in terms of correlation, monotonicity and accuracy for both single-database and cross-database evaluations.
The ITAB is an interactive aptitude battery that measures fluid intelligence through game-like tests. It aims to assess intelligence in a way that is engaging for test-takers who dislike traditional tests. The ITAB measures how well users incorporate feedback to solve problems efficiently. It provides scores on prediction, work style, and intelligence that can be customized. Research shows the ITAB predicts technical training outcomes incrementally over an ASVAB composite. The ITAB is a valid, reliable tool for measuring fluid intelligence relevant for technical jobs.
Exploring Support Vector Regression - Signals and Systems ProjectSurya Chandra
Our team competed in a Kaggle competition to predict the bike share use as a part of their capital bike share program in Washington DC using a powerful function approximation technique called support vector regression.
This document summarizes an analysis of using Support Vector Regression (SVR) to predict bike rental data from a bike sharing program in Washington D.C. It begins with an introduction to SVR and the bike rental prediction competition. It then shows that linear regression performs poorly on this non-linear problem. The document explains how SVR maps data into higher dimensions using kernel functions to allow for non-linear fits. It concludes by outlining the derivation of the SVR method using kernel functions to simplify calculations for the regression.
An application of artificial intelligent neural network and discriminant anal...Alexander Decker
This document presents a study that compares the predictive abilities of artificial neural networks and linear discriminant analysis for credit scoring. A credit dataset from a Nigerian bank with 200 applicants and 15 variables is used to build both neural network and linear discriminant models. The models are evaluated based on measures like accuracy, Wilks' lambda, and canonical correlation. Key findings are that the neural network model performs slightly better with less misclassification cost. However, variable selection is important for both models' success. Age, length of service, and other borrowing are found to be the most important predictor variables.
Boost Your Data Expertise - What's New in Minitab 19.2020.1Minitab, LLC
The ability to derive and communicate meaningful information from your data is a critical skill, particularly now when we all must work smarter and more efficiently. You have more data available to you and your organization than ever before, but are you tapping into it effectively using the best visualizations and analytics?
Learn how to improve your data literacy and expertise using our latest version of Minitab Statistical Software. We’ll help you discover new and improved ways to find trusted insights and learn to communicate them better, faster and easier.
You’ll also learn how Classification and Regression Trees (CART®) will expand your predictive analytics capabilities to better enable you to proactively make decisions.
IRJET - A Survey on Machine Learning Intelligence Techniques for Medical ...IRJET Journal
This document discusses machine learning techniques for classifying medical datasets. It provides an overview of various artificial intelligence and machine learning algorithms that have been applied for medical dataset classification, including artificial neural networks, support vector machines, k-nearest neighbors, and decision trees. The document surveys works that have used these techniques for diseases like breast cancer, heart disease, and diabetes. It also describes common pre-processing steps for medical datasets like data normalization and feature selection methods like F-score and PCA that are used to select the most important features for classification. The classification algorithms are then evaluated based on accuracy metrics like sensitivity, specificity, and accuracy.
sarisus hdyses can create targeted .pptx13DikshaDatir
Customer segmentation is a crucial technique used by businesses to better understand their customer base and tailor their marketing strategies accordingly. By dividing customers into distinct groups based on shared characteristics or behaviors, businesses can create targeted marketing campaigns and improve customer satisfaction.
Software analytics focuses on analyzing and modeling a rich source of software data using well-established data analytics techniques in order to glean actionable insights for improving development practices, productivity, and software quality. However, if care is not taken when analyzing and modeling software data, the predictions and insights that are derived from analytical models may be inaccurate and unreliable. The goal of this hands-on tutorial is to guide participants on how to (1) analyze software data using statistical techniques like correlation analysis, hypothesis testing, effect size analysis, and multiple comparisons, (2) develop accurate, reliable, and reproducible analytical models, (3) interpret the models to uncover relationships and insights, and (4) discuss pitfalls associated with analytical techniques including hands-on examples with real software data. R will be the primary programming language. Code samples will be available in a public GitHub repository. Participants will do exercises via either RStudio or Jupyter Notebook through Binder.
This document summarizes recent developments in Mahout's recommender systems since the publication of Mahout in Action over two years ago. It describes new single machine recommenders, factor models, and item-based collaborative filtering algorithms that can be computed in parallel on Hadoop. Experimental results show these new methods can analyze the Yahoo! Songs dataset of 700 million ratings across 26 machines in under 40 minutes for item similarities and 2 minutes per iteration for matrix factorization.
Here are the key advantages and disadvantages of arrays:
Advantages of arrays:
- Increased directivity - Arrays allow signals to be reinforced in desired directions while cancelled in others, providing improved directivity over a single antenna.
- Increased gain - The increased directivity of an array leads to higher gain compared to a single antenna. This allows for longer communication ranges.
- Beam steering - By adjusting the phase or time delay of signals to each antenna, the main transmission direction of an array can be steered electronically without moving physical elements.
Disadvantages of arrays:
- Increased complexity - Arrays require additional hardware for power distribution to each antenna element, as well as phase/time delay control circuitry
This document provides an overview of Six Sigma methodology. It discusses that Six Sigma aims to reduce defects to 3.4 per million opportunities by using statistical methods. The Six Sigma methodology uses the DMAIC process which stands for Define, Measure, Analyze, Improve, and Control. It also outlines several statistical tools used in Six Sigma like check sheets, Pareto charts, histograms, scatter diagrams and control charts. Process capability and its measures like Cp, Cpk are also explained. The document provides examples to demonstrate how to calculate these metrics and interpret them.
This document provides an overview of Six Sigma methodology. It discusses that Six Sigma aims to reduce defects to 3.4 per million opportunities by using statistical methods. The Six Sigma methodology uses the DMAIC process which stands for Define, Measure, Analyze, Improve, and Control. It also outlines several statistical tools used in Six Sigma like check sheets, Pareto charts, histograms, scatter diagrams, and control charts. Process capability and its measures like Cp, Cpk are also defined. The document aims to explain the key concepts and tools used in Six Sigma to improve quality and processes.
Prediction research: perspectives on performance Stanford 19May22.pptxEwout Steyerberg
Ewout W. Steyerberg discusses performance assessment of predictive models and linking traditional methods to machine learning developments. Key points include: (1) Calibration is the prime property to assess for clinical prediction models to avoid misleading patients or harming decision-making; (2) Most new markers only provide small improvements to prediction, so their incremental value needs careful evaluation; (3) Machine learning methods do not always outperform traditional models, and differences in calibration and clinical usefulness need further study to determine where and how machine learning can best improve prediction.
This document compares several supervised machine learning classification algorithms on a Titanic dataset: Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine, and Naive Bayes. It finds that Random Forest achieves the highest accuracy. Evaluation metrics like precision, recall, F1-score, and accuracy are used to evaluate and compare model performance on test data.
This document compares several supervised machine learning classification algorithms on a Titanic dataset: Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine, and Naive Bayes. It finds that Random Forest achieves the highest accuracy. It preprocesses the dataset, trains models on a training set, and evaluates them using metrics like precision, recall, and F1-score calculated from the confusion matrix on a test set.
Boosting conversion rates on ecommerce using deep learning algorithmsArmando Vieira
This document summarizes an approach to use deep learning algorithms to predict the probability that online shoppers will purchase a product based on their website interactions. The approach involves using stacked auto-encoders to reduce the high dimensionality of the product interaction data before applying classification algorithms. Testing on various datasets showed that random forest outperformed logistic regression and that incorporating time data and more training examples improved prediction performance. Further work proposed applying stacked auto-encoders and deep belief networks to fully leverage the large amount of product interaction data.
IRJET-Handwritten Digit Classification using Machine Learning ModelsIRJET Journal
This document summarizes a research paper that compares different machine learning models for classifying handwritten digits using the MNIST dataset. It finds that a Support Vector Machine Classifier achieves the highest accuracy of 98.3% compared to 96.3% for a Random Forest Classifier and 88.97% for a Logistic Regression model. The paper preprocesses the images, implements the three classifiers, and evaluates their performance based on accuracy and other metrics like precision and recall. It concludes that the SVM Classifier is the most accurate for classifying handwritten digits from the MNIST dataset.
Study on Evaluation of Venture Capital Based onInteractive Projection Algorithminventionjournals
International Journal of Business and Management Invention (IJBMI) is an international journal intended for professionals and researchers in all fields of Business and Management. IJBMI publishes research articles and reviews within the whole field Business and Management, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Week 2 Individual Assignment 2: Quantitative Analysis of Credit -
Solution
s
This assignment is based on the data we used during our two live sessions, but it has been updated to include a splitting variable (credit2.xlsx). In the spreadsheet under the tab “Data," you will find data
pertaining to 1,000 personal loan accounts. The tab “Data Dictionary” contains a description of what the various variables mean.
As a part of a new credit application, the company collects information about the applicant. The company then decides an amount of the credit extended (the variable CREDIT_EXTENDED). For these 1,000 accounts, we also have information on how profitable each account turned out to be (the variable NPV). A negative value indicates a net loss, and this typically happens when the debtor defaults on his/her payments.
The goal in this assignment is to investigate how one can use this data to better manage the bank's credit extension program. Specifically, our goal is to develop a classification model to classify a new credit account as “profitable” or “not profitable." Secondly we want to compare its performance in the context of decision support to a linear regression model that predicts NPV directly.
Please answer all the questions. Supply supporting documentation and show calculations as
needed. Please submit a single, well-formatted PDF or Word file. The instructor should not need to go searching for your answers! In addition, please upload an Excel file with your model outputs – the file will not be graded, but will help the instructor give you feedback, if your model differs substantially from the solutions.
For extra assistance, you may want to access the tutorials located on the course resource center page.Data Preparation
The data preparation repeats the steps from the live session:
a) The goal is to predict whether or not a new credit will result in a profitable account. Create a new variable to use as the dependent variable.
b) Create dummy variables for all categorical variables with more than 2 values (or if you prefer, you can sort your variables into numerical and categorical when you run the model).
c) Split the data into 2 parts using the splitting variable that has been added to the data set. This is to ensure a more balanced split between the validation and training samples. Note that Analytic Solver Data Mining only allows 50 columns in the analysis, so leave out your base dummies (if you created them) when partitioning. After the data partition, you should have 666 rows in your training data and 334 in your validation data.
The Assignment
1. Applying Logistic Regression
If one fits a Logistic Regression Model using all the independent variables, one observes a) a gap in the classification performance between the training data and the validation data, and b) very
high p-values for some of the variables. The performance gap between the training and validation may be a sign of overfitting, and the high p-values may b ...
FREE- REFERENCE IMAGE QUALITY ASSESSMENT FRAMEWORK USING METRICS FUSION AND D...sipij
The document presents a new no-reference image quality assessment framework that uses metrics fusion and dimensionality reduction. It extracts features from three existing no-reference metrics operating in different domains (DCT, DWT, spatial). Singular value decomposition is used to select the most relevant features, which are then input to a relevance vector machine to predict overall quality scores. Experimental results on two databases show the proposed method performs well in terms of correlation, monotonicity and accuracy for both single-database and cross-database evaluations.
The ITAB is an interactive aptitude battery that measures fluid intelligence through game-like tests. It aims to assess intelligence in a way that is engaging for test-takers who dislike traditional tests. The ITAB measures how well users incorporate feedback to solve problems efficiently. It provides scores on prediction, work style, and intelligence that can be customized. Research shows the ITAB predicts technical training outcomes incrementally over an ASVAB composite. The ITAB is a valid, reliable tool for measuring fluid intelligence relevant for technical jobs.
Exploring Support Vector Regression - Signals and Systems ProjectSurya Chandra
Our team competed in a Kaggle competition to predict the bike share use as a part of their capital bike share program in Washington DC using a powerful function approximation technique called support vector regression.
This document summarizes an analysis of using Support Vector Regression (SVR) to predict bike rental data from a bike sharing program in Washington D.C. It begins with an introduction to SVR and the bike rental prediction competition. It then shows that linear regression performs poorly on this non-linear problem. The document explains how SVR maps data into higher dimensions using kernel functions to allow for non-linear fits. It concludes by outlining the derivation of the SVR method using kernel functions to simplify calculations for the regression.
An application of artificial intelligent neural network and discriminant anal...Alexander Decker
This document presents a study that compares the predictive abilities of artificial neural networks and linear discriminant analysis for credit scoring. A credit dataset from a Nigerian bank with 200 applicants and 15 variables is used to build both neural network and linear discriminant models. The models are evaluated based on measures like accuracy, Wilks' lambda, and canonical correlation. Key findings are that the neural network model performs slightly better with less misclassification cost. However, variable selection is important for both models' success. Age, length of service, and other borrowing are found to be the most important predictor variables.
Boost Your Data Expertise - What's New in Minitab 19.2020.1Minitab, LLC
The ability to derive and communicate meaningful information from your data is a critical skill, particularly now when we all must work smarter and more efficiently. You have more data available to you and your organization than ever before, but are you tapping into it effectively using the best visualizations and analytics?
Learn how to improve your data literacy and expertise using our latest version of Minitab Statistical Software. We’ll help you discover new and improved ways to find trusted insights and learn to communicate them better, faster and easier.
You’ll also learn how Classification and Regression Trees (CART®) will expand your predictive analytics capabilities to better enable you to proactively make decisions.
IRJET - A Survey on Machine Learning Intelligence Techniques for Medical ...IRJET Journal
This document discusses machine learning techniques for classifying medical datasets. It provides an overview of various artificial intelligence and machine learning algorithms that have been applied for medical dataset classification, including artificial neural networks, support vector machines, k-nearest neighbors, and decision trees. The document surveys works that have used these techniques for diseases like breast cancer, heart disease, and diabetes. It also describes common pre-processing steps for medical datasets like data normalization and feature selection methods like F-score and PCA that are used to select the most important features for classification. The classification algorithms are then evaluated based on accuracy metrics like sensitivity, specificity, and accuracy.
sarisus hdyses can create targeted .pptx13DikshaDatir
Customer segmentation is a crucial technique used by businesses to better understand their customer base and tailor their marketing strategies accordingly. By dividing customers into distinct groups based on shared characteristics or behaviors, businesses can create targeted marketing campaigns and improve customer satisfaction.
Software analytics focuses on analyzing and modeling a rich source of software data using well-established data analytics techniques in order to glean actionable insights for improving development practices, productivity, and software quality. However, if care is not taken when analyzing and modeling software data, the predictions and insights that are derived from analytical models may be inaccurate and unreliable. The goal of this hands-on tutorial is to guide participants on how to (1) analyze software data using statistical techniques like correlation analysis, hypothesis testing, effect size analysis, and multiple comparisons, (2) develop accurate, reliable, and reproducible analytical models, (3) interpret the models to uncover relationships and insights, and (4) discuss pitfalls associated with analytical techniques including hands-on examples with real software data. R will be the primary programming language. Code samples will be available in a public GitHub repository. Participants will do exercises via either RStudio or Jupyter Notebook through Binder.
This document summarizes recent developments in Mahout's recommender systems since the publication of Mahout in Action over two years ago. It describes new single machine recommenders, factor models, and item-based collaborative filtering algorithms that can be computed in parallel on Hadoop. Experimental results show these new methods can analyze the Yahoo! Songs dataset of 700 million ratings across 26 machines in under 40 minutes for item similarities and 2 minutes per iteration for matrix factorization.
Here are the key advantages and disadvantages of arrays:
Advantages of arrays:
- Increased directivity - Arrays allow signals to be reinforced in desired directions while cancelled in others, providing improved directivity over a single antenna.
- Increased gain - The increased directivity of an array leads to higher gain compared to a single antenna. This allows for longer communication ranges.
- Beam steering - By adjusting the phase or time delay of signals to each antenna, the main transmission direction of an array can be steered electronically without moving physical elements.
Disadvantages of arrays:
- Increased complexity - Arrays require additional hardware for power distribution to each antenna element, as well as phase/time delay control circuitry
This document provides an overview of Six Sigma methodology. It discusses that Six Sigma aims to reduce defects to 3.4 per million opportunities by using statistical methods. The Six Sigma methodology uses the DMAIC process which stands for Define, Measure, Analyze, Improve, and Control. It also outlines several statistical tools used in Six Sigma like check sheets, Pareto charts, histograms, scatter diagrams and control charts. Process capability and its measures like Cp, Cpk are also explained. The document provides examples to demonstrate how to calculate these metrics and interpret them.
This document provides an overview of Six Sigma methodology. It discusses that Six Sigma aims to reduce defects to 3.4 per million opportunities by using statistical methods. The Six Sigma methodology uses the DMAIC process which stands for Define, Measure, Analyze, Improve, and Control. It also outlines several statistical tools used in Six Sigma like check sheets, Pareto charts, histograms, scatter diagrams, and control charts. Process capability and its measures like Cp, Cpk are also defined. The document aims to explain the key concepts and tools used in Six Sigma to improve quality and processes.
Prediction research: perspectives on performance Stanford 19May22.pptxEwout Steyerberg
Ewout W. Steyerberg discusses performance assessment of predictive models and linking traditional methods to machine learning developments. Key points include: (1) Calibration is the prime property to assess for clinical prediction models to avoid misleading patients or harming decision-making; (2) Most new markers only provide small improvements to prediction, so their incremental value needs careful evaluation; (3) Machine learning methods do not always outperform traditional models, and differences in calibration and clinical usefulness need further study to determine where and how machine learning can best improve prediction.
This document compares several supervised machine learning classification algorithms on a Titanic dataset: Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine, and Naive Bayes. It finds that Random Forest achieves the highest accuracy. Evaluation metrics like precision, recall, F1-score, and accuracy are used to evaluate and compare model performance on test data.
This document compares several supervised machine learning classification algorithms on a Titanic dataset: Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine, and Naive Bayes. It finds that Random Forest achieves the highest accuracy. It preprocesses the dataset, trains models on a training set, and evaluates them using metrics like precision, recall, and F1-score calculated from the confusion matrix on a test set.
Boosting conversion rates on ecommerce using deep learning algorithmsArmando Vieira
This document summarizes an approach to use deep learning algorithms to predict the probability that online shoppers will purchase a product based on their website interactions. The approach involves using stacked auto-encoders to reduce the high dimensionality of the product interaction data before applying classification algorithms. Testing on various datasets showed that random forest outperformed logistic regression and that incorporating time data and more training examples improved prediction performance. Further work proposed applying stacked auto-encoders and deep belief networks to fully leverage the large amount of product interaction data.
IRJET-Handwritten Digit Classification using Machine Learning ModelsIRJET Journal
This document summarizes a research paper that compares different machine learning models for classifying handwritten digits using the MNIST dataset. It finds that a Support Vector Machine Classifier achieves the highest accuracy of 98.3% compared to 96.3% for a Random Forest Classifier and 88.97% for a Logistic Regression model. The paper preprocesses the images, implements the three classifiers, and evaluates their performance based on accuracy and other metrics like precision and recall. It concludes that the SVM Classifier is the most accurate for classifying handwritten digits from the MNIST dataset.
Similar to Unit2_Regression, ADVANCED ANALYTICAL THEORY AND METHODS USING PYTHON (20)
Accident detection system project report.pdfKamal Acharya
The Rapid growth of technology and infrastructure has made our lives easier. The
advent of technology has also increased the traffic hazards and the road accidents take place
frequently which causes huge loss of life and property because of the poor emergency facilities.
Many lives could have been saved if emergency service could get accident information and
reach in time. Our project will provide an optimum solution to this draw back. A piezo electric
sensor can be used as a crash or rollover detector of the vehicle during and after a crash. With
signals from a piezo electric sensor, a severe accident can be recognized. According to this
project when a vehicle meets with an accident immediately piezo electric sensor will detect the
signal or if a car rolls over. Then with the help of GSM module and GPS module, the location
will be sent to the emergency contact. Then after conforming the location necessary action will
be taken. If the person meets with a small accident or if there is no serious threat to anyone’s
life, then the alert message can be terminated by the driver by a switch provided in order to
avoid wasting the valuable time of the medical rescue team.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Digital Twins Computer Networking Paper Presentation.pptxaryanpankaj78
A Digital Twin in computer networking is a virtual representation of a physical network, used to simulate, analyze, and optimize network performance and reliability. It leverages real-time data to enhance network management, predict issues, and improve decision-making processes.
Home security is of paramount importance in today's world, where we rely more on technology, home
security is crucial. Using technology to make homes safer and easier to control from anywhere is
important. Home security is important for the occupant’s safety. In this paper, we came up with a low cost,
AI based model home security system. The system has a user-friendly interface, allowing users to start
model training and face detection with simple keyboard commands. Our goal is to introduce an innovative
home security system using facial recognition technology. Unlike traditional systems, this system trains
and saves images of friends and family members. The system scans this folder to recognize familiar faces
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Unit2_Regression, ADVANCED ANALYTICAL THEORY AND METHODS USING PYTHON
1. Course – Big Data Analytics (Professional
Elective-II)
Course code-IT314B
Unit-II- ADVANCED ANALYTICAL THEORY AND
METHODS USING PYTHON
Sanjivani Rural Education Society’s
Sanjivani College of Engineering, Kopargaon-423603
(An Autonomous Institute Affiliated to Savitribai Phule Pune University, Pune)
NAAC ‘A’ Grade Accredited, ISO 9001:2015 Certified
Department of Information Technology
(NBA Accredited)
Mr. Rajendra N Kankrale
Asst. Prof.
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2. BDA- Unit-II Regression Department of IT
Unit-II- ADVANCED ANALYTICAL THEORY AND METHODS USING
PYTHON
• Syllabus
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ADVANCED ANALYTICAL THEORY AND METHODS USING PYTHON
Introduction to Scikit-learn,
Installations, Dataset, matplotlib, filling missing values,
Regression and Classification using Scikit-learn
Association Rules: FP growth,
Regression: Linear Regression, Logistic Regression,
Classification: Naïve Bayes classifier
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Unit-II- Regression
• Motivation
• Regression estimates the relationship between the target and
the independent variable.
• It is used to find the trends in data.
• It helps to predict real/continuous values.
• By performing the regression, we can confidently determine the
most important factor, the least important factor, and how each
factor is affecting the other factors.
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5. BDA- Unit-II Regression Department of IT
Linear Regression
• Linear Regression is a supervised machine learning algorithm.
• It tries to find out the best linear relationship that describes the data you have.
• It assumes that there exists a linear relationship between a dependent variable and
independent variable(s).
• The value of the dependent variable of a linear regression model is a continuous
value i.e. real numbers.
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Representing Linear Regression Model
• Linear regression model represents the linear relationship between a dependent
variable and independent variable(s) via a sloped straight line
• The sloped straight line representing the linear relationship that fits the given
data best is called as a regression line.
• It is also called as best fit line.
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Types of Linear Regression-
1. Simple Linear Regression
2. Multiple Linear Regression
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Simple Linear Regression
For simple linear regression, the form of the model is-
Y = β0 + β1X
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Here,
Y is a dependent variable.
X is an independent variable.
β0 and β1 are the regression coefficients.
β0 is the intercept or the bias that fixes the offset to a
line.
β1 is the slope or weight that specifies the factor by
which X has an impact on Y.
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Simple Linear Regression
There are following 3 cases possible-
Case-01: β1 < 0
It indicates that variable X has negative impact on Y.
If X increases, Y will decrease and vice-versa.
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Simple Linear Regression
Case-02: β1 = 0
• It indicates that variable X has no impact on Y.
• If X changes, there will be no change in Y.
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11. BDA- Unit-II Regression Department of IT
Simple Linear Regression
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Case-03: β1 > 0
It indicates that variable X has positive impact on Y.
If X increases, Y will increase and vice-versa.
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Multiple Linear Regression-
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In multiple linear regression, the dependent variable depends on more
than one independent variables.
For multiple linear regression, the form of the model is-
Y = β0 + β1X1 + β2X2 + β3X3 + …… + βnXn
Here,
Y is a dependent variable.
X1, X2, …., Xn are independent variables.
β0, β1,…, βn are the regression coefficients.
βj (1<=j<=n) is the slope or weight that specifies the factor
by which Xj has an impact on Y.
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Evaluation metrics for a linear regression model
Mean Squared Error (MSE)
The most common metric for regression tasks is MSE. It has a convex shape. It
is the average of the squared difference between the predicted and actual value.
Since it is differentiable and has a convex shape, it is easier to optimize.
MSE penalizes large errors.
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Evaluation metrics for a linear regression model
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15. BDA- Unit-II Regression Department of IT
Evaluation metrics for a linear regression model
Mean Absolute Error (MAE)
This is simply the average of the absolute difference between the target value
and the value predicted by the model. Not preferred in cases where outliers are
prominent.
MAE does not penalize large errors.
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Evaluation metrics for a linear regression model
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17. BDA- Unit-II Regression Department of IT
Evaluation metrics for a linear regression model
Root Mean Squared Error(RMSE)
As RMSE is clear by the name itself, that it is a simple square root of mean squared
error.
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Evaluation metrics for a linear regression model
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Evaluation metrics for a linear regression model
R-squared explains to what extent the variance of one variable explains
the variance of the second variable. In other words, it measures the
proportion of variance of the dependent variable explained by the
independent variable.
R squared is a popular metric for identifying model accuracy. It tells how
close are the data points to the fitted line generated by a regression
algorithm. A larger R squared value indicates a better fit. This helps us to
find the relationship between the independent variable towards the
dependent variable.
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Evaluation metrics for a linear regression model
• SSE is the sum of the square of the
difference between the actual value
and the predicted value
• SST is the total sum of the square of
the difference between the actual
value and the mean of the actual
value.
• yi is the observed target value, ŷi is
the predicted value, and y-bar is the
mean value, m represents the total
number of observations.
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Evaluation metrics for a linear regression model
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Evaluation metrics for a linear regression model
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• R² score ranges from 0 to 1. The closest to 1 the R², the better the
regression model is. If R² is equal to 0, the model is not performing better
than a random model. If R² is negative, the regression model is erroneous.
• A small MAE suggests the model is great at prediction, while a large MAE
suggests that your model may have trouble in certain areas. MAE of 0
means that your model is a perfect predictor of the outputs.
• If you have outliers in the dataset then it penalizes the outliers most and
the calculated MSE is bigger. So, in short, It is not Robust to outliers which
were an advantage in MAE.
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Simple Linear Regression With scikit-learn
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• You’ll start with the simplest case, which is simple linear regression. There
are five basic steps when you’re implementing linear regression:
1. Import the packages and classes that you need.
2. Provide data to work with, and eventually do appropriate transformations.
3. Create a regression model and fit it with existing data.
4. Check the results of model fitting to know whether the model is
satisfactory.
5. Apply the model for predictions.
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Simple Linear Regression With scikit-learn
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• Step 1: Import packages and classes
• The first step is to import the package numpy and the class
LinearRegression from sklearn.linear_model:
• >>> import numpy as np
• >>> from sklearn.linear_model import LinearRegression
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Simple Linear Regression With scikit-learn
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• Step 2: Provide data
• The second step is defining data to work with. The inputs (regressors, 𝑥)
and output (response, 𝑦) should be arrays or similar objects. This is the
simplest way of providing data for regression:
• >>> x = np.array([5, 15, 25, 35, 45, 55]).reshape((-1, 1))
• >>> y = np.array([5, 20, 14, 32, 22, 38])
• Now, you have two arrays: the input, x, and the output, y. You should call
.reshape() on x because this array must be two-dimensional, or more
precisely, it must have one column and as many rows as necessary. That’s
exactly what the argument (-1, 1) of .reshape() specifies.
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Simple Linear Regression With scikit-learn
26
• This is how x and y look now:
• >>> x
• array([[ 5],
• [15],
• [25],
• [35],
• [45],
• [55]])
• >>> y
• array([ 5, 20, 14, 32, 22, 38])
• As you can see, x has two dimensions, and x.shape is (6, 1), while y has a
single dimension, and y.shape is (6,).
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Simple Linear Regression With scikit-learn
27
• Step 3: Create a model and fit it
• The next step is to create a linear regression model and fit it using the
existing data.
• Create an instance of the class LinearRegression, which will represent the
regression model:
• >>> model = LinearRegression()
• This statement creates the variable model as an instance of
LinearRegression. You can provide several optional parameters to
LinearRegression:
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Simple Linear Regression With scikit-learn
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• This statement creates the variable model as an instance of
LinearRegression. You can provide several optional parameters to
LinearRegression:
• fit_intercept is a Boolean that, if True, decides to calculate the intercept 𝑏₀
or, if False, considers it equal to zero. It defaults to True.
• normalize is a Boolean that, if True, decides to normalize the input
variables. It defaults to False, in which case it doesn’t normalize the input
variables.
• copy_X is a Boolean that decides whether to copy (True) or overwrite the
input variables (False). It’s True by default.
• n_jobs is either an integer or None. It represents the number of jobs used
in parallel computation. It defaults to None, which usually means one job. -
1 means to use all available processors.
• Your model as defined above uses the default values of all parameters.
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Simple Linear Regression With scikit-learn
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• It’s time to start using the model. First, you need to call .fit() on model:
• >>> model.fit(x, y)
• LinearRegression()
• With .fit(), you calculate the optimal values of the weights 𝑏₀ and 𝑏₁, using
the existing input and output, x and y, as the arguments. In other words,
.fit() fits the model. It returns self, which is the variable model itself. That’s
why you can replace the last two statements with this one:
• >>> model = LinearRegression().fit(x, y)
• This statement does the same thing as the previous two. It’s just shorter.
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Simple Linear Regression With scikit-learn
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• Step 4: Get results
• Once you have your model fitted, you can get the results to check whether
the model works satisfactorily and to interpret it.
• You can obtain the coefficient of determination, 𝑅², with .score() called on
model:
• >>> r_sq = model.score(x, y)
• >>> print(f"coefficient of determination: {r_sq}")
• coefficient of determination: 0.7158756137479542
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Simple Linear Regression With scikit-learn
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• When you’re applying .score(), the arguments are also the predictor x and
response y, and the return value is 𝑅².
• The attributes of model are .intercept_, which represents the coefficient 𝑏₀,
and .coef_, which represents 𝑏₁:
• >>> print(f"intercept: {model.intercept_}")
• intercept: 5.633333333333329
• >>> print(f"slope: {model.coef_}")
• slope: [0.54]
• The code above illustrates how to get 𝑏₀ and 𝑏₁. You can notice that
.intercept_ is a scalar, while .coef_ is an array.